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1588 lines
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"cells": [
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"<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/extras/exercises/06_pytorch_transfer_learning_exercises.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
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]
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},
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{
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"cell_type": "markdown",
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"source": [
|
||
"# 06. PyTorch Transfer Learning Exercises\n",
|
||
"\n",
|
||
"Welcome to the 06. PyTorch Transfer Learning exercise template notebook.\n",
|
||
"\n",
|
||
"There are several questions in this notebook and it's your goal to answer them by writing Python and PyTorch code.\n",
|
||
"\n",
|
||
"> **Note:** There may be more than one solution to each of the exercises, don't worry too much about the *exact* right answer. Try to write some code that works first and then improve it if you can.\n",
|
||
"\n",
|
||
"## Resources and solutions\n",
|
||
"\n",
|
||
"* These exercises/solutions are based on [section 06. PyTorch Transfer Learning](https://www.learnpytorch.io/06_pytorch_transfer_learning/) of the Learn PyTorch for Deep Learning course by Zero to Mastery.\n",
|
||
"\n",
|
||
"**Solutions:** \n",
|
||
"\n",
|
||
"Try to complete the code below *before* looking at these.\n",
|
||
"\n",
|
||
"* See a live [walkthrough of the solutions (errors and all) on YouTube](https://youtu.be/ueLolShyFqs).\n",
|
||
"* See an example [solutions notebook for these exercises on GitHub](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/solutions/06_pytorch_transfer_learning_exercise_solutions.ipynb)."
|
||
],
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"metadata": {
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"id": "zNqPNlYylluR"
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}
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},
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{
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"cell_type": "markdown",
|
||
"source": [
|
||
"## 1. Make predictions on the entire test dataset and plot a confusion matrix for the results of our model compared to the truth labels. \n",
|
||
"* **Note:** You will need to get the dataset and the trained model/retrain the model from notebook 06 to perform predictions.\n",
|
||
"* Check out [03. PyTorch Computer Vision section 10](https://www.learnpytorch.io/03_pytorch_computer_vision/#10-making-a-confusion-matrix-for-further-prediction-evaluation) for ideas."
|
||
],
|
||
"metadata": {
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"id": "nwmoMhW8IqSu"
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||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Import required libraries/code\n",
|
||
"import torch\n",
|
||
"import torchvision\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"from torch import nn\n",
|
||
"from torchvision import transforms, datasets\n",
|
||
"\n",
|
||
"# Try to get torchinfo, install it if it doesn't work\n",
|
||
"try:\n",
|
||
" from torchinfo import summary\n",
|
||
"except:\n",
|
||
" print(\"[INFO] Couldn't find torchinfo... installing it.\")\n",
|
||
" !pip install -q torchinfo\n",
|
||
" from torchinfo import summary\n",
|
||
"\n",
|
||
"# Try to import the going_modular directory, download it from GitHub if it doesn't work\n",
|
||
"try:\n",
|
||
" from going_modular.going_modular import data_setup, engine\n",
|
||
"except:\n",
|
||
" # Get the going_modular scripts\n",
|
||
" print(\"[INFO] Couldn't find going_modular scripts... downloading them from GitHub.\")\n",
|
||
" !git clone https://github.com/mrdbourke/pytorch-deep-learning\n",
|
||
" !mv pytorch-deep-learning/going_modular .\n",
|
||
" !rm -rf pytorch-deep-learning\n",
|
||
" from going_modular.going_modular import data_setup, engine"
|
||
],
|
||
"metadata": {
|
||
"id": "nqtAWBUJgaF1",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"outputId": "14cc75f3-e109-4e84-c941-2598df3557b3"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"[INFO] Couldn't find torchinfo... installing it.\n",
|
||
"[INFO] Couldn't find going_modular scripts... downloading them from GitHub.\n",
|
||
"Cloning into 'pytorch-deep-learning'...\n",
|
||
"remote: Enumerating objects: 1708, done.\u001b[K\n",
|
||
"remote: Counting objects: 100% (160/160), done.\u001b[K\n",
|
||
"remote: Compressing objects: 100% (88/88), done.\u001b[K\n",
|
||
"remote: Total 1708 (delta 67), reused 151 (delta 60), pack-reused 1548\u001b[K\n",
|
||
"Receiving objects: 100% (1708/1708), 230.85 MiB | 14.36 MiB/s, done.\n",
|
||
"Resolving deltas: 100% (927/927), done.\n",
|
||
"Checking out files: 100% (124/124), done.\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Setup device agnostic code\n",
|
||
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
|
||
"device"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 35
|
||
},
|
||
"id": "O10_T_xSKJlf",
|
||
"outputId": "fd30e756-e542-4b2b-d974-1ed38451fecf"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"'cuda'"
|
||
],
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "string"
|
||
}
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 2
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Get data "
|
||
],
|
||
"metadata": {
|
||
"id": "nrzg3TaSKLAh"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import os\n",
|
||
"import requests\n",
|
||
"import zipfile\n",
|
||
"\n",
|
||
"from pathlib import Path\n",
|
||
"\n",
|
||
"# Setup path to data folder\n",
|
||
"data_path = Path(\"data/\")\n",
|
||
"image_path = data_path / \"pizza_steak_sushi\"\n",
|
||
"\n",
|
||
"# If the image folder doesn't exist, download it and prepare it... \n",
|
||
"if image_path.is_dir():\n",
|
||
" print(f\"{image_path} directory exists.\")\n",
|
||
"else:\n",
|
||
" print(f\"Did not find {image_path} directory, creating one...\")\n",
|
||
" image_path.mkdir(parents=True, exist_ok=True)\n",
|
||
" \n",
|
||
" # Download pizza, steak, sushi data\n",
|
||
" with open(data_path / \"pizza_steak_sushi.zip\", \"wb\") as f:\n",
|
||
" request = requests.get(\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi.zip\")\n",
|
||
" print(\"Downloading pizza, steak, sushi data...\")\n",
|
||
" f.write(request.content)\n",
|
||
"\n",
|
||
" # Unzip pizza, steak, sushi data\n",
|
||
" with zipfile.ZipFile(data_path / \"pizza_steak_sushi.zip\", \"r\") as zip_ref:\n",
|
||
" print(\"Unzipping pizza, steak, sushi data...\") \n",
|
||
" zip_ref.extractall(image_path)\n",
|
||
"\n",
|
||
" # Remove .zip file\n",
|
||
" os.remove(data_path / \"pizza_steak_sushi.zip\")\n",
|
||
"\n",
|
||
"# Setup Dirs\n",
|
||
"train_dir = image_path / \"train\"\n",
|
||
"test_dir = image_path / \"test\""
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Lt_CNQ4rKPmg",
|
||
"outputId": "a1364d91-3afa-4401-94cb-94e4df837f06"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Did not find data/pizza_steak_sushi directory, creating one...\n",
|
||
"Downloading pizza, steak, sushi data...\n",
|
||
"Unzipping pizza, steak, sushi data...\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Prepare data"
|
||
],
|
||
"metadata": {
|
||
"id": "PGaMWWaoKQlM"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Create a transforms pipeline\n",
|
||
"simple_transform = transforms.Compose([\n",
|
||
" transforms.Resize((224, 224)), # 1. Reshape all images to 224x224 (though some models may require different sizes)\n",
|
||
" transforms.ToTensor(), # 2. Turn image values to between 0 & 1 \n",
|
||
" transforms.Normalize(mean=[0.485, 0.456, 0.406], # 3. A mean of [0.485, 0.456, 0.406] (across each colour channel)\n",
|
||
" std=[0.229, 0.224, 0.225]) # 4. A standard deviation of [0.229, 0.224, 0.225] (across each colour channel),\n",
|
||
"])"
|
||
],
|
||
"metadata": {
|
||
"id": "VNIQNEQVKVXu"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Create training and testing DataLoader's as well as get a list of class names\n",
|
||
"train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
|
||
" test_dir=test_dir,\n",
|
||
" transform=simple_transform, # resize, convert images to between 0 & 1 and normalize them\n",
|
||
" batch_size=32) # set mini-batch size to 32\n",
|
||
"\n",
|
||
"train_dataloader, test_dataloader, class_names"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Njd5lHTcKW23",
|
||
"outputId": "fbc224df-8243-4e7b-90cd-49adc000fd47"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"(<torch.utils.data.dataloader.DataLoader at 0x7f5f520c9bd0>,\n",
|
||
" <torch.utils.data.dataloader.DataLoader at 0x7f5f520c9c90>,\n",
|
||
" ['pizza', 'steak', 'sushi'])"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 5
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Get and prepare a pretrained model"
|
||
],
|
||
"metadata": {
|
||
"id": "Ciw2DiRHKaSE"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Setup the model with pretrained weights and send it to the target device \n",
|
||
"model_0 = torchvision.models.efficientnet_b0(pretrained=True).to(device)\n",
|
||
"#model_0 # uncomment to output (it's very long)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 86,
|
||
"referenced_widgets": [
|
||
"6e25b4bb0d254191a793696a0f4f00ce",
|
||
"37424313e66f474da42cfe1b512f09df",
|
||
"58fd00f6a9114192a4fa757c1f669bff",
|
||
"f115ea4b5fad4bb1910fca49ed3da8a1",
|
||
"e8eba8e353e940ff9287929e41e4d656",
|
||
"bc33539914a947ee89c271f10ea6a2bb",
|
||
"6e03cb60fab94b7e92ce16c8178922dd",
|
||
"5d464254c31d4516899643112fa0e958",
|
||
"06df3ad4b7454556a43b6d61640b12f8",
|
||
"0bdc7325c839439589a16c88876d6bd5",
|
||
"873a483782894789bf0dee546a1b2d50"
|
||
]
|
||
},
|
||
"id": "snUuRXd8Kdk5",
|
||
"outputId": "eac2a1e6-5607-437e-90b5-41639d17e5a8"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Downloading: \"https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet_b0_rwightman-3dd342df.pth\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" 0%| | 0.00/20.5M [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "6e25b4bb0d254191a793696a0f4f00ce"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Freeze all base layers in the \"features\" section of the model (the feature extractor) by setting requires_grad=False\n",
|
||
"for param in model_0.features.parameters():\n",
|
||
" param.requires_grad = False"
|
||
],
|
||
"metadata": {
|
||
"id": "IbRhGvy_KeVL"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Set the manual seeds\n",
|
||
"torch.manual_seed(42)\n",
|
||
"torch.cuda.manual_seed(42)\n",
|
||
"\n",
|
||
"# Get the length of class_names (one output unit for each class)\n",
|
||
"output_shape = len(class_names)\n",
|
||
"\n",
|
||
"# Recreate the classifier layer and seed it to the target device\n",
|
||
"model_0.classifier = torch.nn.Sequential(\n",
|
||
" torch.nn.Dropout(p=0.2, inplace=True), \n",
|
||
" torch.nn.Linear(in_features=1280, \n",
|
||
" out_features=output_shape, # same number of output units as our number of classes\n",
|
||
" bias=True)).to(device)"
|
||
],
|
||
"metadata": {
|
||
"id": "G1-6xV3ZKeSX"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Train model"
|
||
],
|
||
"metadata": {
|
||
"id": "XQFaXX8CKePi"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Define loss and optimizer\n",
|
||
"loss_fn = nn.CrossEntropyLoss()\n",
|
||
"optimizer = torch.optim.Adam(model_0.parameters(), lr=0.001)"
|
||
],
|
||
"metadata": {
|
||
"id": "exxU79eaKeM6"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Set the random seeds\n",
|
||
"torch.manual_seed(42)\n",
|
||
"torch.cuda.manual_seed(42)\n",
|
||
"\n",
|
||
"# Start the timer\n",
|
||
"from timeit import default_timer as timer \n",
|
||
"start_time = timer()\n",
|
||
"\n",
|
||
"# Setup training and save the results\n",
|
||
"model_0_results = engine.train(model=model_0,\n",
|
||
" train_dataloader=train_dataloader,\n",
|
||
" test_dataloader=test_dataloader,\n",
|
||
" optimizer=optimizer,\n",
|
||
" loss_fn=loss_fn,\n",
|
||
" epochs=5,\n",
|
||
" device=device)\n",
|
||
"\n",
|
||
"# End the timer and print out how long it took\n",
|
||
"end_time = timer()\n",
|
||
"print(f\"[INFO] Total training time: {end_time-start_time:.3f} seconds\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 153,
|
||
"referenced_widgets": [
|
||
"ae21171f17de45d895ab7a319dade609",
|
||
"f9c60d9c0aed49faa993fd865fb09174",
|
||
"755366e3f75e44c2b7a79bce78d77d11",
|
||
"4a05e8d965124327a2329cf9e1eec984",
|
||
"fe93ec079b384ac38a6f4d0e505431ff",
|
||
"88dea77f1bcf44ffb69654515ee34f54",
|
||
"2678a567b0414e1d9cfbfc2ecf5ffd30",
|
||
"ce621be138a84f33b24c05b2d9cfd5f0",
|
||
"1fa41d239a3a4845904434d057476a75",
|
||
"f4827c6e36a1463fb0c82347f64230a2",
|
||
"cea8f9c48bd8429998352a090173f537"
|
||
]
|
||
},
|
||
"id": "ComVkVtuKeKG",
|
||
"outputId": "6d43205a-4e9f-4627-999a-40d07380cd58"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" 0%| | 0/5 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "ae21171f17de45d895ab7a319dade609"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Epoch: 1 | train_loss: 1.0894 | train_acc: 0.4492 | test_loss: 0.9214 | test_acc: 0.5085\n",
|
||
"Epoch: 2 | train_loss: 0.8697 | train_acc: 0.7734 | test_loss: 0.8036 | test_acc: 0.7434\n",
|
||
"Epoch: 3 | train_loss: 0.7769 | train_acc: 0.7734 | test_loss: 0.7404 | test_acc: 0.7737\n",
|
||
"Epoch: 4 | train_loss: 0.7244 | train_acc: 0.7422 | test_loss: 0.6488 | test_acc: 0.8864\n",
|
||
"Epoch: 5 | train_loss: 0.6426 | train_acc: 0.7812 | test_loss: 0.6254 | test_acc: 0.8968\n",
|
||
"[INFO] Total training time: 31.032 seconds\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Make predictions on the entire test dataset with the model"
|
||
],
|
||
"metadata": {
|
||
"id": "xFS4lE_IKyE_"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# TODO"
|
||
],
|
||
"metadata": {
|
||
"id": "DwZuCluFu375"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Make a confusion matrix with the test preds and the truth labels"
|
||
],
|
||
"metadata": {
|
||
"id": "Mb2bQ1b5K2WP"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"Need the following libraries to make a confusion matrix:\n",
|
||
"* torchmetrics - https://torchmetrics.readthedocs.io/en/stable/\n",
|
||
"* mlxtend - http://rasbt.github.io/mlxtend/"
|
||
],
|
||
"metadata": {
|
||
"id": "5I2jpYAcM07s"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# See if torchmetrics exists, if not, install it\n",
|
||
"try:\n",
|
||
" import torchmetrics, mlxtend\n",
|
||
" print(f\"mlxtend version: {mlxtend.__version__}\")\n",
|
||
" assert int(mlxtend.__version__.split(\".\")[1]) >= 19, \"mlxtend verison should be 0.19.0 or higher\"\n",
|
||
"except:\n",
|
||
" !pip install -q torchmetrics -U mlxtend # <- Note: If you're using Google Colab, this may require restarting the runtime\n",
|
||
" import torchmetrics, mlxtend\n",
|
||
" print(f\"mlxtend version: {mlxtend.__version__}\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "qcKYZGWuK2P8",
|
||
"outputId": "88c33b26-0b76-42d7-8a27-fb3073b1fc3f"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\u001b[K |████████████████████████████████| 409 kB 7.5 MB/s \n",
|
||
"\u001b[K |████████████████████████████████| 1.3 MB 45.7 MB/s \n",
|
||
"\u001b[?25hmlxtend version: 0.19.0\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Import mlxtend upgraded version\n",
|
||
"import mlxtend \n",
|
||
"print(mlxtend.__version__)\n",
|
||
"assert int(mlxtend.__version__.split(\".\")[1]) >= 19 # should be version 0.19.0 or higher"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "QOYVew4xMxgI",
|
||
"outputId": "d3b393b8-09c3-46f7-c799-2f91ee4d30e6"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"0.19.0\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# TODO"
|
||
],
|
||
"metadata": {
|
||
"id": "_5LU9-5Xu7dP"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## 2. Get the \"most wrong\" of the predictions on the test dataset and plot the 5 \"most wrong\" images. You can do this by:\n",
|
||
"* Predicting across all of the test dataset, storing the labels and predicted probabilities.\n",
|
||
"* Sort the predictions by *wrong prediction* and then *descending predicted probabilities*, this will give you the wrong predictions with the *highest* prediction probabilities, in other words, the \"most wrong\".\n",
|
||
"* Plot the top 5 \"most wrong\" images, why do you think the model got these wrong?\n",
|
||
"\n",
|
||
"You'll want to:\n",
|
||
"* Create a DataFrame with sample, label, prediction, pred prob\n",
|
||
"* Sort DataFrame by correct (does label == prediction)\n",
|
||
"* Sort DataFrame by pred prob (descending)\n",
|
||
"* Plot the top 5 \"most wrong\" image predictions"
|
||
],
|
||
"metadata": {
|
||
"id": "YqlStPo-gbrF"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# TODO"
|
||
],
|
||
"metadata": {
|
||
"id": "cHtMeYHuvDwy"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## 3. Predict on your own image of pizza/steak/sushi - how does the model go? What happens if you predict on an image that isn't pizza/steak/sushi?\n",
|
||
"* Here you can get an image from a website like http://www.unsplash.com to try it out or you can upload your own."
|
||
],
|
||
"metadata": {
|
||
"id": "1IvuTskxgjaw"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# TODO: Get an image of pizza/steak/sushi\n"
|
||
],
|
||
"metadata": {
|
||
"id": "C16glgVFglmG"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# TODO: Get an image of not pizza/steak/sushi\n"
|
||
],
|
||
"metadata": {
|
||
"id": "clA_KmihVYyA"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## 4. Train the model from section 4 in notebook 06 part 3 for longer (10 epochs should do), what happens to the performance?\n",
|
||
"\n",
|
||
"* See the model in notebook 06 part 3 for reference: https://www.learnpytorch.io/06_pytorch_transfer_learning/#3-getting-a-pretrained-model"
|
||
],
|
||
"metadata": {
|
||
"id": "Vzvi8GprgmJ0"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# TODO: Recreate a new model \n"
|
||
],
|
||
"metadata": {
|
||
"id": "kIKg53Jna-Rt"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# TODO: Train the model for 10 epochs"
|
||
],
|
||
"metadata": {
|
||
"id": "JhGT9igPgoF5"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## 5. Train the model from section 4 above with more data, say 20% of the images from Food101 of Pizza, Steak and Sushi images.\n",
|
||
"* You can find the [20% Pizza, Steak, Sushi dataset](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/data/pizza_steak_sushi_20_percent.zip) on the course GitHub. It was created with the notebook [`extras/04_custom_data_creation.ipynb`](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/04_custom_data_creation.ipynb). \n"
|
||
],
|
||
"metadata": {
|
||
"id": "_oRrWPZTgoqL"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Get 20% data"
|
||
],
|
||
"metadata": {
|
||
"id": "VxyMMnUbgvw2"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import os\n",
|
||
"import requests\n",
|
||
"import zipfile\n",
|
||
"\n",
|
||
"from pathlib import Path\n",
|
||
"\n",
|
||
"# Setup path to data folder\n",
|
||
"data_path = Path(\"data/\")\n",
|
||
"image_path = data_path / \"pizza_steak_sushi_20_percent\"\n",
|
||
"image_data_zip_path = \"pizza_steak_sushi_20_percent.zip\"\n",
|
||
"\n",
|
||
"# If the image folder doesn't exist, download it and prepare it... \n",
|
||
"if image_path.is_dir():\n",
|
||
" print(f\"{image_path} directory exists.\")\n",
|
||
"else:\n",
|
||
" print(f\"Did not find {image_path} directory, creating one...\")\n",
|
||
" image_path.mkdir(parents=True, exist_ok=True)\n",
|
||
" \n",
|
||
" # Download pizza, steak, sushi data\n",
|
||
" with open(data_path / image_data_zip_path, \"wb\") as f:\n",
|
||
" request = requests.get(\"https://github.com/mrdbourke/pytorch-deep-learning/raw/main/data/pizza_steak_sushi_20_percent.zip\")\n",
|
||
" print(\"Downloading pizza, steak, sushi data...\")\n",
|
||
" f.write(request.content)\n",
|
||
"\n",
|
||
" # Unzip pizza, steak, sushi data\n",
|
||
" with zipfile.ZipFile(data_path / image_data_zip_path, \"r\") as zip_ref:\n",
|
||
" print(\"Unzipping pizza, steak, sushi 20% data...\") \n",
|
||
" zip_ref.extractall(image_path)\n",
|
||
"\n",
|
||
" # Remove .zip file\n",
|
||
" os.remove(data_path / image_data_zip_path)\n",
|
||
"\n",
|
||
"# Setup Dirs\n",
|
||
"train_dir_20_percent = image_path / \"train\"\n",
|
||
"test_dir_20_percent = image_path / \"test\"\n",
|
||
"\n",
|
||
"train_dir_20_percent, test_dir_20_percent"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "U_fdu5m2eKT9",
|
||
"outputId": "121c61f3-f505-4302-b3b9-8b8bae5b5e1d"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Did not find data/pizza_steak_sushi_20_percent directory, creating one...\n",
|
||
"Downloading pizza, steak, sushi data...\n",
|
||
"Unzipping pizza, steak, sushi 20% data...\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"(PosixPath('data/pizza_steak_sushi_20_percent/train'),\n",
|
||
" PosixPath('data/pizza_steak_sushi_20_percent/test'))"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 33
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Create DataLoaders"
|
||
],
|
||
"metadata": {
|
||
"id": "SQj7eFdSe4Fv"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Create a transforms pipeline\n",
|
||
"simple_transform = transforms.Compose([\n",
|
||
" transforms.Resize((224, 224)), # 1. Reshape all images to 224x224 (though some models may require different sizes)\n",
|
||
" transforms.ToTensor(), # 2. Turn image values to between 0 & 1 \n",
|
||
" transforms.Normalize(mean=[0.485, 0.456, 0.406], # 3. A mean of [0.485, 0.456, 0.406] (across each colour channel)\n",
|
||
" std=[0.229, 0.224, 0.225]) # 4. A standard deviation of [0.229, 0.224, 0.225] (across each colour channel),\n",
|
||
"])"
|
||
],
|
||
"metadata": {
|
||
"id": "TEG_k785e7Jw"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Create training and testing DataLoader's as well as get a list of class names\n",
|
||
"train_dataloader_20_percent, test_dataloader_20_percent, class_names = data_setup.create_dataloaders(train_dir=train_dir_20_percent,\n",
|
||
" test_dir=test_dir_20_percent,\n",
|
||
" transform=simple_transform, # resize, convert images to between 0 & 1 and normalize them\n",
|
||
" batch_size=32) # set mini-batch size to 32\n",
|
||
"\n",
|
||
"train_dataloader_20_percent, test_dataloader_20_percent, class_names"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "82x7LnQJe7H5",
|
||
"outputId": "342fd4e7-0656-495a-aee0-0d23be130438"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"(<torch.utils.data.dataloader.DataLoader at 0x7f5ede28e390>,\n",
|
||
" <torch.utils.data.dataloader.DataLoader at 0x7f5ede28e210>,\n",
|
||
" ['pizza', 'steak', 'sushi'])"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 35
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Get a pretrained model"
|
||
],
|
||
"metadata": {
|
||
"id": "qROl77sKfIOd"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# TODO"
|
||
],
|
||
"metadata": {
|
||
"id": "PHWNZ6yDvpR8"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Train a model with 20% of the data"
|
||
],
|
||
"metadata": {
|
||
"id": "UqffJfOIfp3T"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# TODO"
|
||
],
|
||
"metadata": {
|
||
"id": "wXpYOYeTvp7a"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## 6. Try a different model from [`torchvision.models`](https://pytorch.org/vision/stable/models.html) on the Pizza, Steak, Sushi data, how does this model perform?\n",
|
||
"* You'll have to change the size of the classifier layer to suit our problem.\n",
|
||
"* You may want to try an EfficientNet with a higher number than our B0, perhaps `torchvision.models.efficientnet_b2()`?\n",
|
||
" * **Note:** Depending on the model you use you will have to prepare/transform the data in a certain way."
|
||
],
|
||
"metadata": {
|
||
"id": "Ibj4UPjRgvly"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# TODO "
|
||
],
|
||
"metadata": {
|
||
"id": "3FQ8tL7El7eO"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
}
|
||
]
|
||
} |